


import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def 							UpperCamelCase_(					_A :str	)->		Any:
			UpperCamelCase__       =							[
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_A					,  _A	)
def 							UpperCamelCase_(					_A :Any	)->		List[str]:
			UpperCamelCase__,   UpperCamelCase__       =							emb.weight.shape
			UpperCamelCase__       =							nn.Linear(_A					,  _A					,  bias=_A	)
			UpperCamelCase__       =							emb.weight.data
			return lin_layer
def 							UpperCamelCase_(					_A :Optional[int]					,  _A :List[str]=None	)->		List[str]:
			UpperCamelCase__       =							{}
			for old_key in state_dict.keys():
						UpperCamelCase__       =							old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												UpperCamelCase__       =							key.replace("moe_layer.experts.0"					,  F'''ffn.experts.expert_{expert_idx}'''	)
									else:
												UpperCamelCase__       =							key.replace("moe_layer.experts."					,  "ffn.experts.expert_"	)
						if "gate" in key:
									UpperCamelCase__       =							key.replace(".moe_layer.gate.wg"					,  ".ffn.router.classifier"	)
						if "fc2" and "experts" not in key:
									UpperCamelCase__       =							key.replace(".fc2."					,  ".ffn.fc2."	)
						if "fc1" and "experts" not in key:
									UpperCamelCase__       =							key.replace(".fc1."					,  ".ffn.fc1."	)
						if ".encoder_attn." in key:
									UpperCamelCase__       =							key.replace(".encoder_attn."					,  ".cross_attention."	)
						if "encoder_attn_layer_norm" in key:
									UpperCamelCase__       =							key.replace("encoder_attn_layer_norm"					,  "cross_attention_layer_norm"	)
						if "final_layer_norm" in key:
									UpperCamelCase__       =							key.replace("final_layer_norm"					,  "ff_layer_norm"	)
						UpperCamelCase__       =							state_dict[old_key]
			return new_dict
def 							UpperCamelCase_(					_A :Optional[int]					,  _A :str					,  _A :Tuple					,  _A :Dict					,  _A :str = WEIGHTS_NAME	)->		str:
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0
			os.makedirs(_A					,  exist_ok=_A	)

			for expert in range(_A	):
						UpperCamelCase__       =							switch_checkpoint_path + F'''-rank-{expert}.pt'''
						if os.path.isfile(_A	):
									UpperCamelCase__       =							torch.load(_A	)["model"]
									remove_ignore_keys_(_A	)
									UpperCamelCase__       =							rename_fairseq_keys(_A					,  _A	)
									UpperCamelCase__       =							os.path.join(
									    _A					,  weights_name.replace(".bin"					,  F'''-{len(_A	)+1:05d}-of-???.bin'''	)	)
									torch.save(_A					,  _A	)
									sharded_state_dicts.append(expert_state.keys()	)
									total_size += sum([value.numel() for key, value in expert_state.items()]	) * dtype_byte_size(
									    expert_state[list(_A	)[0]].dtype	)

    # Add the last block
			UpperCamelCase__       =							os.path.join(_A					,  weights_name.replace(".bin"					,  F'''-{len(_A	)+1:05d}-of-???.bin'''	)	)
			UpperCamelCase__       =							torch.load(switch_checkpoint_path + "-shared.pt"	)["model"]
			remove_ignore_keys_(_A	)
			UpperCamelCase__       =							rename_fairseq_keys(_A					,  _A	)
			UpperCamelCase__       =							shared_weights["decoder.embed_tokens.weight"]
			sharded_state_dicts.append(shared_weights.keys()	)

			# If we only have the shared weights (dummy model/experts saved on the same file)
			if len(_A	) == 1:
						UpperCamelCase__       =							os.path.join(_A					,  _A	)
						torch.save(_A					,  _A	)
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(_A					,  _A	)
			# Otherwise, let's build the index
			UpperCamelCase__       =							{}
			for idx, shard in enumerate(_A	):
						UpperCamelCase__       =							weights_name.replace(".bin"					,  F'''-{idx+1:05d}-of-{len(_A	):05d}.bin'''	)
						UpperCamelCase__       =							os.path.join(_A					,  weights_name.replace(".bin"					,  F'''-{idx+1:05d}-of-???.bin'''	)	)
						os.rename(_A					,  os.path.join(_A					,  _A	)	)
						for key in shard:
									UpperCamelCase__       =							shard_file

    # Add the metadata
			UpperCamelCase__       =							{"total_size": total_size}
			UpperCamelCase__       =							{"metadata": metadata, "weight_map": weight_map}

			with open(os.path.join(_A					,  _A	)					,  "w"					,  encoding="utf-8"	) as f:
						UpperCamelCase__       =							json.dumps(_A					,  indent=2					,  sort_keys=_A	) + "\n"
						f.write(_A	)

			return metadata, index


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--nllb_moe_checkpoint_path',
						    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
						    type=str,
						    required=False,
						    help='Path to a directory containing a folder per layer. Follows the original Google format.',
						)
						parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
						    type=str,
						    required=False,
						    help='Path to the output pytorch model.',
						)
						__UpperCamelCase     		=							parser.parse_args()
						__UpperCamelCase		,	__UpperCamelCase     		=							shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_2_8,
						    args.dtype,
						)

						__UpperCamelCase     		=							NllbMoeConfig.from_pretrained(
						    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						__UpperCamelCase     		=							NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)






__UpperCamelCase     		=							2_5_6
# Modulus to hash a string
__UpperCamelCase     		=							1_0_0_0_0_0_3
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		bool:
			UpperCamelCase__       =							len(_A	)
			UpperCamelCase__       =							len(_A	)
			if p_len > t_len:
						return False

			UpperCamelCase__       =							0
			UpperCamelCase__       =							0
			UpperCamelCase__       =							1

			# Calculating the hash of pattern and substring of text
			for i in range(_A	):
						UpperCamelCase__       =							(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
						UpperCamelCase__       =							(ord(text[i]	) + text_hash * alphabet_size) % modulus
						if i == p_len - 1:
									continue
						UpperCamelCase__       =							(modulus_power * alphabet_size) % modulus

			for i in range(0					,  t_len - p_len + 1	):
						if text_hash == p_hash and text[i : i + p_len] == pattern:
									return True
						if i == t_len - p_len:
									continue
						# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
						UpperCamelCase__       =							(
						    (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
						    + ord(text[i + p_len]	)
						) % modulus
			return False
def 							UpperCamelCase_(					)->		None:
			UpperCamelCase__       =							"abc1abc12"
			UpperCamelCase__       =							"alskfjaldsabc1abc1abc12k23adsfabcabc"
			UpperCamelCase__       =							"alskfjaldsk23adsfabcabc"
			assert rabin_karp(_A					,  _A	) and not rabin_karp(_A					,  _A	)

			# Test 2)
			UpperCamelCase__       =							"ABABX"
			UpperCamelCase__       =							"ABABZABABYABABX"
			assert rabin_karp(_A					,  _A	)

			# Test 3)
			UpperCamelCase__       =							"AAAB"
			UpperCamelCase__       =							"ABAAAAAB"
			assert rabin_karp(_A					,  _A	)

			# Test 4)
			UpperCamelCase__       =							"abcdabcy"
			UpperCamelCase__       =							"abcxabcdabxabcdabcdabcy"
			assert rabin_karp(_A					,  _A	)

			# Test 5)
			UpperCamelCase__       =							"Lü"
			UpperCamelCase__       =							"Lüsai"
			assert rabin_karp(_A					,  _A	)
			UpperCamelCase__       =							"Lue"
			assert not rabin_karp(_A					,  _A	)
			print("Success."	)


if __name__ == "__main__":
						test_rabin_karp()


